NOTACON 9: Collaboration. You keep using that word... (EN) | Enh. audio
Speaker: Angela Harms Sure. You collaborate every day at work, right? Except you don't. Because collaboration is not the same as cooperation. Cooperation is where everybody does their part. Collaboration creates a solution that's more than the sum of those parts. Cooperation helps us cho
From playlist Notacon 9
The reliability of machine learning systems in the presence of adversarial noise has become a major field of study in recent years. As ML is being used for increasingly security sensitive applications and is trained in increasingly unreliable data, the ability for learning algorithms to to
From playlist Top 10 Tutorials and Talks: Adversarial Machine Learning
Adversarial Machine Learning Ian Goodfellow
Google's Ian Goodfellow joined us to share his research. Full slides: http://www.iangoodfellow.com/slides/2018-05-24.pdf
From playlist Top 10 Tutorials and Talks: Adversarial Machine Learning
Our desire to build good and lasting friendships is often undermined by a lack of focus on what friendship should really be about. Getting clear about what friendship is for isn’t cynical; it provides the foundation for genuine bonds. If you like our films, take a look at our shop (we shi
From playlist RELATIONSHIPS
Adversarial Question Answering: How Explanations for Humans can Trick Computers [Lecture]
NAACL Workshop: https://dadcworkshop.github.io/ 2018 Adversarial Event: https://sites.google.com/view/qanta/past-events/dec-15-2018 This is a single lecture from a course. If you you like the material and want more context (e.g., the lectures that came before), check out the whole cours
From playlist Computational Linguistics I
Adversarial Examples Are Not Bugs, They Are Features
Abstract: Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. We demonstrate that adversarial examples can be directly attributed to the presence of non-robust features: features derived from p
From playlist Adversarial Examples
NOTACON 9: Collaboration. You keep using that word... (EN)
Speaker: Angela Harms Sure. You collaborate every day at work, right? Except you don't. Because collaboration is not the same as cooperation. Cooperation is where everybody does their part. Collaboration creates a solution that's more than the sum of those parts. Cooperation helps us cho
From playlist Notacon 9
The Secrets of Other People's Relationships
Those of us in relationships suffer from an ignorance of what other people’s relationships are really like. We should recognise that episodes of difficulty and ambivalence are not the exception, but the norm. Sign up to our mailing list to receive 10% off your first order with us: https:/
From playlist RELATIONSHIPS
How to Become a Better Collaborator
In this video, you’ll learn tips and strategies for better collaboration. Visit https://edu.gcfglobal.org/en/creativity/how-to-become-a-better-collaborator/1/ for more information. We hope you enjoy!
From playlist Creativity
Introduction to GANs, NIPS 2016 | Ian Goodfellow, OpenAI
NIPS 2016 Workshop on Adversarial Training http://www.iangoodfellow.com/slides/2016-12-9-gans.pdf https://arxiv.org/abs/1701.00160 Introduction to Generative Adversarial Networks
From playlist Introduction to Deep Learning
AI & Multiagent Systems Research for Social Good - Prof. Milind Tambe
Recorded July 19th, 2018 Milind Tambe is Helen N. and Emmett H. Jones Professor in Engineering and a Professor of Computer Science and Industrial and Systems Engineering at the University of Southern California, Los Angeles.
From playlist AI talks
ShmooCon 2012: Lessons of the Kobayashi Maru: Cheating is Fundamental (EN)
Speakers: James Caroland | Greg Conti Every day security professionals face off against adversaries who do not play by the rules. However, at every turn in life we are taught to never... ever... cheat. Traditional information security education and training programs further compound the p
From playlist ShmooCon 2012
Network design games in presence of strategic adversaries by Prithwish Basu
Games, Epidemics and Behavior URL: http://www.icts.res.in/discussion_meeting/geb2016/ DATES: Monday 27 Jun, 2016 - Friday 01 Jul, 2016 VENUE : Madhava lecture hall, ICTS Bangalore DESCRIPTION: The two main goals of this Discussion Meeting are: 1. To explore the foundations of policy d
From playlist Games, Epidemics and Behavior
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples | AISC
A.I. Socratic Circles (formerly TDLS) https://aisc.a-i.science/events/2019-03-21/ Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples Deep neural networks (DNNs) have demonstrated impressive performance on a wide array of tasks, but they are usually considered
From playlist Explainability and Ethics
Cronus: Robust Knowledge Transfer for Federated Learning
A Google TechTalk, 2020/7/29, presented by Reza Shokri, National University of Singapore ABSTRACT: Federated learning is vulnerable to many known privacy and security attacks. Shared parameters leak a significant amount of information about the participants’ private datasets, as they conti
From playlist 2020 Google Workshop on Federated Learning and Analytics
Course Overview | Stanford CS224U Natural Language Understanding | Spring 2021
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai To learn more about this course visit: https://online.stanford.edu/courses/cs224u-natural-language-understanding To follow along with the course schedule and sy
From playlist Stanford CS224U: Natural Language Understanding | Spring 2021
CERIAS Security: On the Evolution of Adversary Models for Security Protocols 4/6
Clip 4/6 Speaker: Virgil D. Gligor · University of Maryland Invariably, new technologies introduce new vulnerabilities which, in principle, enable new attacks by increasingly potent adversaries. Yet new systems are more adept at handling well-known attacks by old adversaries than anti
From playlist The CERIAS Security Seminars 2006
A Generative Adversarial Network for E-commerce by Arijit Biswas
DISCUSSION MEETING THE THEORETICAL BASIS OF MACHINE LEARNING (ML) ORGANIZERS: Chiranjib Bhattacharya, Sunita Sarawagi, Ravi Sundaram and SVN Vishwanathan DATE : 27 December 2018 to 29 December 2018 VENUE : Ramanujan Lecture Hall, ICTS, Bangalore ML (Machine Learning) has enjoyed tr
From playlist The Theoretical Basis of Machine Learning 2018 (ML)
Semantic Adversarial Attacks for Privacy Protection
A Google TechTalk, 2020/7/30, presented byAli Shahin Shamsabadi, Ricardo Sanchez-Matilla, Andrea Cavallaro, Queen Mary University of London ABSTRACT: Images shared on social media are routinely analyzed by machine learning models for content annotation and user profiling. These automatic
From playlist 2020 Google Workshop on Federated Learning and Analytics
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Career Challenges